Defining sub-regions in locally sparsified compressive sensing MRI Razzaq, Fuleah A., Mohamed, Shady, Bhatti, Asim and Nahavandi, Saeid 2013, Defining sub-regions in locally sparsified compressive sensing MRI, in BioMed 2013 : Proceedings of the 10th IASTED International Conference on Biomedical Engineering, ACTA Press, Calgary, Alb., pp. 360-367, doi: 10.2316/P.2013.791-045. Attached Files Name Description MIMEType Size Downloads Title Defining sub-regions in locally sparsified compressive sensing MRI Author(s) Razzaq, Fuleah A. Mohamed, Shady orcid.org/0000-0002-8851-1635 Bhatti, Asim orcid.org/0000-0001-6876-1437 Nahavandi, Saeid Conference name IASTED Biomedical Engineering. Conference (10th : 2013 : Innsbruck, Austria) Conference location Innsbruck, Austria Conference dates 13-15 Feb. 2013 Title of proceedings BioMed 2013 : Proceedings of the 10th IASTED International Conference on Biomedical Engineering Editor(s) Boccaccini, A.R. Publication date 2013 Conference series IASTED Biomedical Engineering Conference Start page 360 End page 367 Total pages 8 Publisher ACTA Press Place of publication Calgary, Alb. Keyword(s) magnetic resonance imaging compressive sensing sparse signals fourier transform signal-to noise ratio (SNR) L I minimization Summary Magnetic Resonance Imaging (MRI) is an important imaging technique. However, it is a time consuming process. The aim of this study is to make the imaging process ef?cient. MR images are sparse in the sensing domain and Compressive Sensing exploits this sparsity. Locally sparsi?ed Compressed Sensing is a specialized case of CS which sub-divides the image and sparsi?es each region separately; later samples are taken based on sparsity level in that region. In this paper, a new structured approach is presented for de?ning the size and locality of sub-regions in image. Experiments were done on the regions de?ned by proposed framework and local sparsity constraints were used to achieve high sparsity level and to reduce the sample set. Experimental results and their comparison with global CS is presented in the paper. ISBN 9780889869424 Language eng DOI 10.2316/P.2013.791-045 Field of Research 080401 Coding and Information Theory Socio Economic Objective 929999 Health not elsewhere classified HERDC Research category E1 Full written paper - refereed Copyright notice ©2013, ACTA Press Persistent URL http://hdl.handle.net/10536/DRO/DU:30057138 Document type: Conference Paper Collection: Centre for Intelligent Systems Research Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 23 Oct 2013, 09:58:11 EST Wed, 23 Oct 2013, 14:06:40 EST Wed, 23 Oct 2013, 14:09:45 EST Thu, 31 Oct 2013, 13:30:24 EST Thu, 31 Oct 2013, 15:41:27 EST Fri, 01 Nov 2013, 08:12:36 EST Tue, 12 Aug 2014, 11:04:05 EST Wed, 27 Aug 2014, 23:47:31 EST Wed, 21 Oct 2015, 14:51:43 EST Wed, 18 Nov 2015, 07:25:18 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 3 times in Scopus Search Google Scholar Access Statistics: 312 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Wed, 23 Oct 2013, 09:58:11 EST

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